Merge remote-tracking branch 'upstream/develop' into nd/rot90

Author: stdlib-bot

.gitignore

@@ -25,6 +25,7 @@ package.json.copy
 ###############
 build/
 downloads/
+plans/
 reports/
 tmp/
 
@@ -175,6 +176,10 @@ acs-*.bib
 *.ind
 *.ist
 
+# Git #
+#######
+.worktrees
+
 # Visual Studio #
 #################
 .vscode/

docs/migration-guides/numpy/README.md

@@ -35,10 +35,10 @@ limitations under the License.
 | Broadcast a scalar to a specified shape | `np.broadcast_to(np.array(scalar), shape)` | [`broadcastScalar(scalar, shape)`][@stdlib/ndarray/broadcast-scalar] |
 | Copy an array | `np.copy(x)` | [`copy(x)`][@stdlib/ndarray/copy] |
 | Count the number of falsy values in an array | `x.size-np.count_nonzero(x)` | [`countFalsy(x)`][@stdlib/ndarray/count-falsy] |
-| Count the number of truthy values in an array | `np.count_nonzeros(x)` | [`countTruthy(x)`][@stdlib/ndarray/count-truthy] |
+| Count the number of truthy values in an array | `np.count_nonzero(x)` | [`countTruthy(x)`][@stdlib/ndarray/count-truthy] |
 | Test whether an array includes a specific value | `np.any(np.equal(x,v))` | [`includes(x,v)`][@stdlib/ndarray/includes] |
 | Reverse the elements along a dimension | `np.flip(x, axis=dim)` | [`reverseDimension(x, dim)`][@stdlib/ndarray/reverse-dimension] |
-| Prepend a a specified number of singleton dimensions | `np.reshape(x, (1,)*n + x.shape)` | [`prependSingletonDimensions(x, n)`][@stdlib/ndarray/prepend-singleton-dimensions] |
+| Prepend a specified number of singleton dimensions | `np.reshape(x, (1,)*n + x.shape)` | [`prependSingletonDimensions(x, n)`][@stdlib/ndarray/prepend-singleton-dimensions] |
 | Test whether an array contains at least `n` truthy values | `np.count_nonzero(x) >= n` | [`some(x, n)`][@stdlib/ndarray/some] |
 
 <!-- lint enable table-pipe-alignment -->

lib/node_modules/@stdlib/blas/base/ndarray/README.md

@@ -51,6 +51,7 @@ The namespace exposes the following APIs:
 -   <span class="signature">[`daxpy( arrays )`][@stdlib/blas/base/ndarray/daxpy]</span><span class="delimiter">: </span><span class="description">multiply a one-dimensional double-precision floating-point ndarray `x` by a constant `alpha` and add the result to a one-dimensional double-precision floating-point ndarray `y`.</span>
 -   <span class="signature">[`dcopy( arrays )`][@stdlib/blas/base/ndarray/dcopy]</span><span class="delimiter">: </span><span class="description">copy values from a one-dimensional double-precision floating-point ndarray `x` into a one-dimensional double-precision floating-point ndarray `y`.</span>
 -   <span class="signature">[`ddot( arrays )`][@stdlib/blas/base/ndarray/ddot]</span><span class="delimiter">: </span><span class="description">calculate the dot product of two one-dimensional double-precision floating-point ndarrays.</span>
+-   <span class="signature">[`dswap( arrays )`][@stdlib/blas/base/ndarray/dswap]</span><span class="delimiter">: </span><span class="description">interchange two one-dimensional double-precision floating-point ndarrays.</span>
 -   <span class="signature">[`gasum( arrays )`][@stdlib/blas/base/ndarray/gasum]</span><span class="delimiter">: </span><span class="description">calculate the sum of absolute values for all elements in a one-dimensional ndarray.</span>
 -   <span class="signature">[`gaxpy( arrays )`][@stdlib/blas/base/ndarray/gaxpy]</span><span class="delimiter">: </span><span class="description">multiply a one-dimensional ndarray `x` by a constant `alpha` and add the result to a one-dimensional ndarray `y`.</span>
 -   <span class="signature">[`gcopy( arrays )`][@stdlib/blas/base/ndarray/gcopy]</span><span class="delimiter">: </span><span class="description">copy values from a one-dimensional ndarray `x` into a one-dimensional ndarray `y`.</span>
@@ -115,6 +116,8 @@ console.log( objectKeys( ns ) );
 
 [@stdlib/blas/base/ndarray/ddot]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/ndarray/ddot
 
+[@stdlib/blas/base/ndarray/dswap]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/ndarray/dswap
+
 [@stdlib/blas/base/ndarray/gasum]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/ndarray/gasum
 
 [@stdlib/blas/base/ndarray/gaxpy]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/ndarray/gaxpy

lib/node_modules/@stdlib/blas/base/ndarray/caxpy/README.md

@@ -41,28 +41,30 @@ var caxpy = require( '@stdlib/blas/base/ndarray/caxpy' );
 Multiplies a one-dimensional single-precision complex floating-point ndarray `x` by a constant `alpha` and adds the result to a one-dimensional single-precision complex floating-point ndarray `y`.
 
 ```javascript
-var Complex64Array = require( '@stdlib/array/complex64' );
+var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
 var Complex64 = require( '@stdlib/complex/float32/ctor' );
-var scalar2ndarray = require( '@stdlib/ndarray/base/from-scalar' );
-var ndarray = require( '@stdlib/ndarray/base/ctor' );
-
-var xbuf = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
-var x = new ndarray( 'complex64', xbuf, [ 3 ], [ 1 ], 0, 'row-major' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
 
-var ybuf = new Complex64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
-var y = new ndarray( 'complex64', ybuf, [ 3 ], [ 1 ], 0, 'row-major' );
+var x = new Complex64Vector( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
+var y = new Complex64Vector( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
 
-var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), 'complex64', 'row-major' );
+var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), {
+    'dtype': 'complex64'
+});
 var z = caxpy( [ x, y, alpha ] );
 // returns <ndarray>[ <Complex64>[ -2.0, 5.0 ], <Complex64>[ -4.0, 11.0 ], <Complex64>[ -6.0, 17.0 ] ]
 
-var bool = ( y === z );
+var bool = ( z === y );
 // returns true
 ```
 
 The function has the following parameters:
 
--   **arrays**: array-like object containing an input ndarray, an output ndarray, and a zero-dimensional ndarray containing a scalar constant.
+-   **arrays**: array-like object containing the following ndarrays in order:
+
+    -   input ndarray
+    -   output ndarray
+    -   zero-dimensional ndarray containing a scalar constant
 
 </section>
 
@@ -82,23 +84,20 @@ The function has the following parameters:
 
 ```javascript
 var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
-var Complex64Array = require( '@stdlib/array/complex64' );
+var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
 var Complex64 = require( '@stdlib/complex/float32/ctor' );
 var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
-var ndarray = require( '@stdlib/ndarray/base/ctor' );
 var ndarray2array = require( '@stdlib/ndarray/to-array' );
 var caxpy = require( '@stdlib/blas/base/ndarray/caxpy' );
 
 var opts = {
     'dtype': 'float32'
 };
 
-var xbuf = new Complex64Array( discreteUniform( 10, 0, 100, opts ) );
-var x = new ndarray( 'complex64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
+var x = new Complex64Vector( discreteUniform( 10, 0, 100, opts ) );
 console.log( ndarray2array( x ) );
 
-var ybuf = new Complex64Array( discreteUniform( xbuf.length*2, 0, 10, opts ) );
-var y = new ndarray( 'complex64', ybuf, [ ybuf.length ], [ 1 ], 0, 'row-major' );
+var y = new Complex64Vector( discreteUniform( 10, 0, 10, opts ) );
 console.log( ndarray2array( y ) );
 
 var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), {

lib/node_modules/@stdlib/blas/base/ndarray/caxpy/benchmark/benchmark.js

@@ -25,9 +25,8 @@ var uniform = require( '@stdlib/random/array/uniform' );
 var isnanf = require( '@stdlib/math/base/assert/is-nanf' );
 var realf = require( '@stdlib/complex/float32/real' );
 var pow = require( '@stdlib/math/base/special/pow' );
-var ndarray = require( '@stdlib/ndarray/base/ctor' );
-var scalar2ndarray = require( '@stdlib/ndarray/base/from-scalar' );
-var Complex64Array = require( '@stdlib/array/complex64' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
 var Complex64 = require( '@stdlib/complex/float32/ctor' );
 var format = require( '@stdlib/string/format' );
 var pkg = require( './../package.json' ).name;
@@ -60,14 +59,14 @@ function createBenchmark( len ) {
 	xbuf = uniform( len*2, -100.0, 100.0, {
 		'dtype': 'float32'
 	});
-	x = new ndarray( options.dtype, new Complex64Array( xbuf ), [ len ], [ 1 ], 0, 'row-major' );
+	x = new Complex64Vector( xbuf.buffer );
 
 	ybuf = uniform( len*2, -100.0, 100.0, {
 		'dtype': 'float32'
 	});
-	y = new ndarray( options.dtype, new Complex64Array( ybuf ), [ len ], [ 1 ], 0, 'row-major' );
+	y = new Complex64Vector( ybuf.buffer );
 
-	alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), options.dtype, 'row-major' );
+	alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), options );
 
 	return benchmark;
 

lib/node_modules/@stdlib/blas/base/ndarray/caxpy/docs/repl.txt

@@ -20,17 +20,10 @@
 
     Examples
     --------
-    > var xbuf = new {{alias:@stdlib/array/complex64}}( [ 4.0, 2.0, -3.0, 5.0 ] );
-    > var ybuf = new {{alias:@stdlib/array/complex64}}( [ 2.0, 6.0, -1.0, -4.0 ] );
-    > var dt = 'complex64';
-    > var sh = [ xbuf.length ];
-    > var st = [ 1 ];
-    > var oo = 0;
-    > var ord = 'row-major';
-    > var x = new {{alias:@stdlib/ndarray/ctor}}( dt, xbuf, sh, st, oo, ord );
-    > var y = new {{alias:@stdlib/ndarray/ctor}}( dt, ybuf, sh, st, oo, ord );
+    > var x = new {{alias:@stdlib/ndarray/vector/complex64}}( [ 4.0, 2.0, -3.0, 5.0 ] );
+    > var y = new {{alias:@stdlib/ndarray/vector/complex64}}( [ 2.0, 6.0, -1.0, -4.0 ] );
     > var ca = new {{alias:@stdlib/complex/float32/ctor}}( 1.0, 2.0 );
-    > var alpha = {{alias:@stdlib/ndarray/base/from-scalar}}( ca, dt, ord );
+    > var alpha = {{alias:@stdlib/ndarray/from-scalar}}( ca, { 'dtype': 'complex64' } );
 
     > {{alias}}( [ x, y, alpha ] );
     > y

lib/node_modules/@stdlib/blas/base/ndarray/caxpy/docs/types/index.d.ts

@@ -29,18 +29,16 @@ import { complex64ndarray } from '@stdlib/types/ndarray';
 * @returns output ndarray
 *
 * @example
-* var Complex64Array = require( '@stdlib/array/complex64' );
+* var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
 * var Complex64 = require( '@stdlib/complex/float32/ctor' );
-* var scalar2ndarray = require( '@stdlib/ndarray/base/from-scalar' );
-* var ndarray = require( '@stdlib/ndarray/base/ctor' );
+* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
 *
-* var xbuf = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 ] );
-* var x = new ndarray( 'complex64', xbuf, [ 5 ], [ 1 ], 0, 'row-major' );
+* var x = new Complex64Vector( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 ] );
+* var y = new Complex64Vector( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
 *
-* var ybuf = new Complex64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
-* var y = new ndarray( 'complex64', ybuf, [ 5 ], [ 1 ], 0, 'row-major' );
-*
-* var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), 'complex64', 'row-major' );
+* var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), {
+*     'dtype': 'complex64'
+* });
 *
 * var z = caxpy( [ x, y, alpha ] );
 * // returns <ndarray>[ <Complex64>[ -2.0, 5.0 ], <Complex64>[ -4.0, 11.0 ], <Complex64>[ -6.0, 17.0 ], <Complex64>[ -8.0, 23.0 ], <Complex64>[ -10.0, 29.0 ] ]

lib/node_modules/@stdlib/blas/base/ndarray/caxpy/examples/index.js

@@ -19,23 +19,20 @@
 'use strict';
 
 var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
-var Complex64Array = require( '@stdlib/array/complex64' );
+var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
 var Complex64 = require( '@stdlib/complex/float32/ctor' );
 var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
-var ndarray = require( '@stdlib/ndarray/base/ctor' );
 var ndarray2array = require( '@stdlib/ndarray/to-array' );
 var caxpy = require( './../lib' );
 
 var opts = {
 	'dtype': 'float32'
 };
 
-var xbuf = new Complex64Array( discreteUniform( 10, 0, 100, opts ) );
-var x = new ndarray( 'complex64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
+var x = new Complex64Vector( discreteUniform( 10, 0, 100, opts ) );
 console.log( ndarray2array( x ) );
 
-var ybuf = new Complex64Array( discreteUniform( xbuf.length*2, 0, 10, opts ) );
-var y = new ndarray( 'complex64', ybuf, [ ybuf.length ], [ 1 ], 0, 'row-major' );
+var y = new Complex64Vector( discreteUniform( 10, 0, 10, opts ) );
 console.log( ndarray2array( y ) );
 
 var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), {

lib/node_modules/@stdlib/blas/base/ndarray/caxpy/lib/index.js

@@ -24,22 +24,20 @@
 * @module @stdlib/blas/base/ndarray/caxpy
 *
 * @example
-* var Complex64Array = require( '@stdlib/array/complex64' );
+* var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
 * var Complex64 = require( '@stdlib/complex/float32/ctor' );
-* var scalar2ndarray = require( '@stdlib/ndarray/base/from-scalar' );
-* var ndarray = require( '@stdlib/ndarray/base/ctor' );
+* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
 * var caxpy = require( '@stdlib/blas/base/ndarray/caxpy' );
 *
-* var xbuf = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );
-* var x = new ndarray( 'complex64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );
+* var x = new Complex64Vector( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 ] );
+* var y = new Complex64Vector( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
 *
-* var ybuf = new Complex64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
-* var y = new ndarray( 'complex64', ybuf, [ 4 ], [ 1 ], 0, 'row-major' );
-*
-* var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), 'complex64', 'row-major' );
+* var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), {
+*     'dtype': 'complex64'
+* });
 *
 * var z = caxpy( [ x, y, alpha ] );
-* // returns <ndarray>[ <Complex64>[ -2.0, 5.0 ], <Complex64>[ -4.0, 11.0 ], <Complex64>[ -6.0, 17.0 ], <Complex64>[ -8.0, 23.0 ] ]
+* // returns <ndarray>[ <Complex64>[ -2.0, 5.0 ], <Complex64>[ -4.0, 11.0 ], <Complex64>[ -6.0, 17.0 ], <Complex64>[ -8.0, 23.0 ], <Complex64>[ -10.0, 29.0 ] ]
 *
 * var bool = ( z === y );
 * // returns true

lib/node_modules/@stdlib/blas/base/ndarray/caxpy/lib/main.js

@@ -37,21 +37,19 @@ var strided = require( '@stdlib/blas/base/caxpy' ).ndarray;
 * @returns {Object} output ndarray
 *
 * @example
-* var Complex64Array = require( '@stdlib/array/complex64' );
+* var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
 * var Complex64 = require( '@stdlib/complex/float32/ctor' );
-* var scalar2ndarray = require( '@stdlib/ndarray/base/from-scalar' );
-* var ndarray = require( '@stdlib/ndarray/base/ctor' );
+* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
 *
-* var xbuf = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );
-* var x = new ndarray( 'complex64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );
+* var x = new Complex64Vector( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 ] );
+* var y = new Complex64Vector( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
 *
-* var ybuf = new Complex64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
-* var y = new ndarray( 'complex64', ybuf, [ 4 ], [ 1 ], 0, 'row-major' );
-*
-* var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), 'complex64', 'row-major' );
+* var alpha = scalar2ndarray( new Complex64( 1.0, 2.0 ), {
+*     'dtype': 'complex64'
+* });
 *
 * var z = caxpy( [ x, y, alpha ] );
-* // returns <ndarray>[ <Complex64>[ -2.0, 5.0 ], <Complex64>[ -4.0, 11.0 ], <Complex64>[ -6.0, 17.0 ], <Complex64>[ -8.0, 23.0 ] ]
+* // returns <ndarray>[ <Complex64>[ -2.0, 5.0 ], <Complex64>[ -4.0, 11.0 ], <Complex64>[ -6.0, 17.0 ], <Complex64>[ -8.0, 23.0 ], <Complex64>[ -10.0, 29.0 ] ]
 *
 * var bool = ( z === y );
 * // returns true